Methods and systems for employing artificial intelligence in automated orthodontic diagnosis &amp; treatment planning

ABSTRACT

Methods and systems for diagnosing and identifying a treatment for an orthodontic condition are disclosed. Such methods and systems generally entail the use of a server on which a centralized website is hosted. The server is configured to receive patient data through the website. The methods and systems further include the use of a database that includes or has access to (i) information derived from textbooks and scientific literature and (ii) dynamic results derived from ongoing and completed patient treatments. The methods and systems further include the operation of at least one computer program within the server, which is capable of analyzing the patient data and identifying at least one diagnosis of the orthodontic condition. The methods and systems further entail assigning a probability value to the at least one diagnosis, with the probability value representing a likelihood that the diagnosis is accurate. The methods and systems further include instructing the computer program to identify at least one treatment approach, a corrective appliance, or a combination thereof for the at least one diagnosis.

FIELD OF THE INVENTION

The field of the present invention generally relates to methods andsystems that may be used to diagnose an orthodontic condition. Moreparticularly, the field of the present invention relates to methods andsystems for automatically diagnosing, and proposing a treatment for, anorthodontic condition, which methods and systems employ the use ofartificial intelligence capabilities.

BACKGROUND OF THE INVENTION

Many systems and methods have been developed or, more typically,envisioned which, hypothetically, could automate the capture of patientdata and diagnosis of an orthodontic condition. These actual (orcontemplated) systems employ certain components and subsystems that mayautomate the capture of patient data (such as orthodontic images orscans), the transfer of such data to an orthodontist, and/or even theinterpretation of such data (or, more typically, discrete portions ofsuch data). However, the currently-available methods and systems fail tocomprise an ability to make decisions based on interpreted data, in anautomated fashion. In other words, the currently-available methods andsystems do not comprise an effective, accurate, and efficient“artificial intelligence” capability, in the automated diagnosis andtreatment of an orthodontic condition.

The present invention addresses these shortcomings of thecurrently-available systems for automated orthodontic diagnosis andtreatment.

SUMMARY OF THE INVENTION

According to certain aspects of the present invention, methods andsystems for diagnosing and identifying a treatment for an orthodonticcondition are provided. Such methods and systems generally comprise theuse of a server on which a centralized website is hosted. The server isconfigured to receive patient data through the website, with suchpatient data comprising patient photographs, study models, radiographs,and/or combinations thereof. The methods and systems further comprisethe use of a database that includes or has access to (i) informationderived from textbooks and scientific literature and (ii) dynamicresults derived from ongoing and completed patient treatments.

The invention provides that at least one computer program will operatewithin the server, which is capable of analyzing the patient data andidentifying at least one diagnosis of the orthodontic condition (basedon the information derived from textbooks and scientific literature,dynamic results derived from ongoing and completed patient treatments,or combinations thereof). The methods and systems further compriseassigning a probability value to at least one diagnosis, with theprobability value representing a likelihood that the diagnosis isaccurate. According to such embodiments, the methods and systems of theinvention further comprise instructing the computer program to identifyat least one treatment approach, a corrective appliance, or acombination thereof for the at least one diagnosis.

The above-mentioned and additional features of the present invention arefurther illustrated in the Detailed Description contained herein.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1: a diagram illustrating the general steps and processesencompassed by the present invention, namely, the generation of patientdata, the analysis of such data by one or more servers, and theautomated diagnosis of an orthodontic condition and the proposedtreatment approaches therefor.

FIG. 2: a diagram illustrating the various components of the systemsdescribed herein, namely, the centralized website, server, and databasedescribed herein.

FIG. 3: a table summarizing the results of a root tip analysis of apatient.

FIG. 4: a table summarizing the results of a tooth torque analysis of apatient.

FIG. 5: a table summarizing the results of an arch length analysis of apatient.

DETAILED DESCRIPTION OF THE INVENTION

The following will describe, in detail, several preferred embodiments ofthe present invention. These embodiments are provided by way ofexplanation only, and thus, should not unduly restrict the scope of theinvention. In fact, those of ordinary skill in the art will appreciateupon reading the present specification and viewing the present drawingsthat the invention teaches many variations and modifications, and thatnumerous variations of the invention may be employed, used, and madewithout departing from the scope and spirit of the invention.

Referring to FIG. 1, according to certain embodiments of the presentinvention, automated diagnosis of an orthodontic condition begins withthe production of patient-specific data, which may comprise patientphotographs 2, study models 4, radiographs 6, and/or combinationsthereof. The types of data captured for a particular patient may be thesame for all patients, or may be customized for each patient. The“orthodontic condition,” referenced herein, may generally comprise anarrangement of a patient's teeth that is undesirable according toapplicable orthodontic standards, whereby such arrangement may beundesirable for medical, orthodontic, aesthetic, and other reasons.Examples of such orthodontic conditions include, but are not limited to,overbites, crossbites, openbites, overjets, underbites, and the like.

These patient data may then be provided to a server 8 through acentralized website 10. Referring to FIG. 2, such data may be providedto the server 8 vis-à-vis an on-line form (within a centralized website10) through which the data may be uploaded and transferred to the server8, or through a constant data feed through a standard Internetconnection. As described herein, the server 8 will preferably comprisecertain tools 12 for analysis and interpretation of such data—and formaking intelligent and probabilistic diagnosis and proposed treatmentsfor an orthodontic condition.

The invention provides that the server 8 will preferably be capable ofcommunicating with at least one database 14 (or group of databases). Thedatabase 14 will preferably store and/or have access to knowledge andinformation derived from scientific, medical, and orthodontic textbooksand literature 16. More particularly, the invention provides that asingle database 14 may store all of such information—or, alternatively,it may store portions of such information and the server 8 may haveaccess to additional information that may be stored within otherdatabases.

According to certain preferred embodiments, the invention willpreferably employ a systematic approach to evaluating the strength ofscientific evidence that may be retrieved from the database 14 describedherein, for the purpose of diagnosing an orthodontic condition (asdescribed below). For example, the server 8 may consider the quality,quantity and consistency of the evidence to derive a grade or confidencelevel of the available knowledge. The invention provides that variouscriteria, such as indirect supporting evidence, may be taken intoaccount in assessing the strength of each piece of scientific evidence.The scientific evidence may then be ranked, based on the grade levels(or confidence levels) assigned thereto.

More particularly, for example, the invention may consider the strongestevidence (i.e., evidence of higher grade levels) being derived from atleast one systematic review of one or more well-designed and randomizedcontrolled trials. The invention provides that a second highest grademay be assigned to, for example, evidence derived from at least oneproperly designed randomized controlled trial, which involved anappropriate sample size and statistical power. The invention furtherprovides that a third highest grade may be assigned to evidence derivedfrom well-designed trials, without randomization; a single grouppre-post, cohort, time series study; or matched case-controlled studies.Still further, the invention provides that a fourth grade may beassigned to evidence from well-designed, non-experimental studies,carried out by more than one center or research group. A fifth andlowest grade of evidence may consist of opinions of respectedauthorities (which are based on clinical evidence), and/or descriptivestudies or reports of expert committees.

The invention provides that the database 14 will further comprise, orhave access to, information that represents dynamic results from ongoingand previously completed orthodontic studies 18. Preferably, thesedynamic results 18 will be organized by orthodontic condition, such thatthe most relevant information may be retrieved as quickly as possible,within the database 14. Similar to the information derived fromscientific, medical, and orthodontic textbooks and literature 16, theinvention provides that all of the dynamic results 18 may be storedwithin the database 14 or, alternatively, portions thereof may be storedwithin the database 14 and other dynamic results 18 may be retrieved, asneeded, from third party databases.

Upon providing the server 8 with the patient data, e.g., patientphotographs 2, study models 4, radiographs 6, and/or combinationsthereof, a user may instruct the server 8 to conduct an automateddiagnosis. The automated diagnosis will be based upon the patient data,the information derived from scientific textbooks and literature 16, anddynamic results from ongoing and previously completed orthodonticstudies 18. The server 8 will preferably employ the use of logic-basedrules and decision trees 20 to diagnose an orthodontic condition basedon all of such information. The invention provides that the server 8will preferably express the diagnosis by identifying one or moreorthodontic conditions, along with a probability value for eachorthodontic condition. According to such embodiments, the probabilityvalue would represent the relative probability that the diagnosis isaccurate.

Still further, the server 8 will be configured to output (recommend) oneor more treatment approaches and/or corrective orthodontic appliances22. More particularly, for each diagnosis 20 identified by the server 8,the server 8 will propose one or more treatment approaches, correctiveappliances, or combinations thereof 22. The invention provides that eachsuch proposed treatment approach and corrective appliance will becorrelated with a probability value. The invention provides that thisprobability value will represent the probability of the proposedtreatment approach and/or appliance correcting the diagnosed orthodonticcondition.

The invention further provides that a user may input patient preferences24 and/or orthodontist-specified preferences to the server 8 (throughthe centralized website 10). For example, the invention provides that apatient may filter the proposed treatments and corrective applianceresults 26 based on cost, or the relative aesthetics of an appliance.Similarly, an orthodontist may filter the proposed treatments andcorrective appliance results 26 based on his/her bias—e.g., anorthodontist may instruct the server 8 to only consider, or to notconsider, a certain type of corrective appliance. Upon completion of theforegoing process, the server may be instructed to generate a report 28,which preferably summarizes the patient data, the diagnoses andassociated probability values, the proposed treatment approaches and/orcorrective devices (and the probability values associated therewith),and any patient and orthodontist preferences that were considered duringthe analysis.

According to certain embodiments, the invention provides that the server8 is configured to analyze the patient data by identifying a locationand position of a plurality of teeth in the patient data intwo-dimensional space or, even more preferably, in three-dimensionalspace (provided that the type and amount of patient data provided to theserver 8 is sufficient to do so). The invention provides that the server8 may be configured to undertake this analysis automatically or,according to certain embodiments, the centralized website 10 willprovide users with certain on-line tools to specify the location andposition of the plurality of teeth in the patient data. For example,such on-line tools may be used to identify, within the patient data, thelocation and position of a patient's incisors, canines, premolars andmolars, as shown within the patient data that has been provided to theserver 8. The location, position, contours, and size of the plurality ofteeth may be mapped out by such user within the centralized website 10,while the user is viewing the patient data that has been uploaded to theserver 8, e.g., using a graphics tool that allows a user to, forexample, approximately trace or identify the outer boundaries of eachtooth.

According to such embodiments, the server 8 may be further configured toassign coordinates to each tooth within the plurality of teeth. Theinvention provides that such coordinates are preferably correlated tothe location and position of each tooth, as automatically determined bythe server (or as otherwise identified by a clinician, using the on-linepatient data analysis tools, described above). According to theseembodiments, the invention provides that the coordinates for each of theplurality of teeth may then be compared (by the server 8) to a tablecontained within the database 14. The table will preferably comprise aseries of diagnostic data sets, with each diagnostic data set comprisingcoordinates, or a range of coordinates, which are correlated with (1) aknown location and position of a plurality of teeth and (2) a previouslydiagnosed orthodontic condition (which previous diagnoses are derivedfrom (a) information derived from textbooks and scientific literatureand (b) dynamic results derived from ongoing and completed patienttreatments).

According to such embodiments, the server 8 may then be instructed toidentify a diagnostic data set contained within the database 14 thatrepresents a statistical “best fit,” or most closely resembles, thecoordinates for the plurality of teeth of the patient. At this point,the server 8 may be instructed to diagnosis the orthodontic conditionbased on the “best fit” diagnostic data set that it identified. Asmentioned above, the server 8 may further assign a probability value tothis diagnosis. The probability value will preferably be based, at leastin part, on a confidence level that has been assigned to the diagnosticdata set which the server identifies as the statistical best fit for thecoordinates for the plurality of teeth of the patient. This confidencelevel will preferably be influenced by the grade level that is assignedto the evidence that supports a connection between the orthodonticcondition that is correlated with the particular diagnostic data set, asdescribed above.

According to certain embodiments, the server 8 or, more particularly,the computer program housed therein, may be instructed to identify atleast one treatment approach, a corrective appliance, or a combinationthereof for the at least one diagnosis that is derived from thepatient's data. This step may be carried by, for example, instructingthe server 8 to calculate a set of target coordinates, which represent adesired and corrected location and position of each tooth in theplurality of teeth of the patient. Based on the target coordinates, thecurrent location and position coordinates of the patient's teeth, andthe diagnosed orthodontic position, the server 8 may be instructed toidentify at least one treatment approach, a corrective appliance, or acombination thereof, which will be effective to reorient the pluralityof teeth towards the location and position represented by the targetcoordinates. The server 8 may further be instructed to calculate aprobability value that is correlated with a relative likelihood of theat least one treatment approach, corrective appliance, or a combinationthereof, being effective to reorient the plurality of teeth to alocation and position represented by the target coordinates.

According to certain preferred embodiments, the invention willpreferably employ certain additional algorithms in analyzing patientdata, diagnosing orthodontic conditions and probability values therefor,and proposing treatment approaches and corrective appliances (andprobability values therefor). By way of illustration, as mentionedabove, the server 8 may be configured to assign greater value (weight)to existing scientific and medical knowledge, relative to dynamicresults from ongoing and completed treatments—when diagnosing andproviding recommended treatment protocols for patients. The followingwill describe certain non-limiting examples of algorithms, which may beemployed in the processes and systems of the present invention.

The invention provides that artificial intelligence algorithms willpreferably be employed in order to create an artificial neural network,which will enable the server to perform the orthodontic diagnosis,treatment planning and prognostication steps described herein. Thealgorithms may utilize statistical estimation, optimization and controltheory methodology, or combinations thereof. In the case of statisticalestimation methods, estimators and estimation methods that may beemployed include, but are not limited to, the following: maximumlikelihood estimators, Bayes estimators, method of moments estimators,Cramer-Rao bound, minimum mean squared error (also known as Bayes leastsquared error), maximum a posteriori, minimum variance unbiasedestimator, best linear unbiased estimator, unbiased estimators, particlefilter, Markov chain Monte Carlo, Kalman filter, Ensemble Kalman filter,and Wiener filter. The statistical optimization techniques that may beutilized include single-variable optimizations or, more preferably,multi-variable optimization techniques. The statistical optimizationmethods may include, but are not limited to, the following: Bundlemethods, Conjugate gradient method, Ellipsoid method, Frank-Wolfemethod, Gradient descent (also known as steepest descent or steepestascent), Interior point methods, Line search, Nelder-Mead method,Newton's method, Quasi-Newton methods, Simplex method and Subgradientmethod.

Because the systems and methods of the present invention involve certaininput provided by users of the invention, the systems and methods aredynamic. As such, the invention provides that algorithms that employcontrol theory may be employed to solve problems in connection with theorthodontic diagnosis, treatment planning and prognostication stepsdescribed herein. Non-limiting examples of such control theory methodsinclude: Adaptive control, Hierarchical control, Intelligent control,Optimal control, Robust control and Stochastic control.

EXAMPLES

Example of Optimization Algorithm for Decision Making in Diagnosis andTreatment Planning.

An important aspect of multiple optimization is the handling of humanpreferences, such as the type of cost- and aesthetic-related preferencesthat a patient or orthodontist may provide to the system describedherein. Although selection or prioritizing alternatives from a set ofavailable options with respect to multiple criteria termedMulti-Criteria Decision Making (MCDM) is an effective optimizationapproach, in practical applications, alternative ratings and criteriaweights can not always be precisely assessed due to unquantifiable,incomplete, and/or unobtainable information—or because of a lack ofknowledge that may cause subjectiveness and vagueness in decisionperformance. As such, the invention provides that the application offuzzy set theory to MCDM models provides an effective solution fordealing with subjectiveness and vagueness commonly found with clinicalinformation. In such embodiments, the invention provides that humanpreferences—from both patient and clinician—may be assigned “utilityvalues” in which a scaled real number is assigned to indicate itsrelative importance. The resulting weighting vector, which evaluatescriteria of decision making, is then provided in fuzzy linguistic termssuch as very poor, poor, fair, good, and very good.

Example of Decision Tree Algorithm for Decision Making in Diagnosis andTreatment Planning.

The invention provides that a decision tree method referred to as“C4.5,” which allows for input of continuous numerical data, ispreferably employed in the methods and systems described herein. Theinvention provides that, under this approach, a decision tree may be“learned” vis-à-vis splitting a source set into subsets, based on anattribute value test. The invention provides that this process may berepeated on each derived subset in a recursive manner, which iscompleted when the subset (at a node) has the same value of the targetvariable, or when splitting no longer adds value to predictions.According to this embodiment, decision trees are used for relativelysimpler functions as decision-tree learners create over-complex trees(overfitting), although pruning may, optionally, be performed tominimize this problem. In addition, concepts that are relatively moredifficult to learn are not easily expressed by decision trees—and, insuch case, more advanced algorithms will be implemented in the systemsand methods described herein.

Example of Partially Observable Markov Decision Processes (POMDPs) andVariants Thereof.

The invention provides that POMDPs are preferably used in the clinicalapplications described herein, particularly for decisions that are madebased on incomplete information. The invention provides that POMDPs arepreferably advantageous insofar as they facilitate the combination ofpatient data, e.g., patient data derived from examination, photographs,radiographs and any other diagnostic aids—as well as the current stateof knowledge of the cause-and-effect representation from these data andmeasurements. The invention provides that feature selection may beperformed using pattern recognition techniques and, furthermore, thetreatment decisions with which to restore the patient to a moredesirable or ideal state are produced.

Patient Example.

The following example describes the application of the processesdescribed herein to a patient in need of orthodontic diagnosis andtreatment. The process begins with the patient undergoing cephalometricradiographic analysis. The data generated by such analysis are presentedin the table below.

Measurement Patient SNA (degrees) 82° SNB (degrees) 74° ANB (degrees) 8° Maxillary incisor to NA (degrees) 22° Maxillary incisor to NA(millimeters) 6 mm Mandibular incisor to NB (degrees) 24° Mandibularincisor to NB (millimeters) 4 mm Pogonion to NB (millimeters) 4 mmMaxillary incisor to Mandibular incisor (degrees) 140°  Occlusal planeto SN (degrees) 15° Go-Gn to SN (degrees) 32° Mandibular incisor to MP(degrees) 86°

Those of ordinary skill in the art will appreciate that thecephalometric radiographic analysis may be performed to capturemeasurements, other than those specified above. However, themeasurements summarized in the table above are often important to anyorthodontic diagnosis. Next, the patient's dentition may be analyzed andmeasured. The table below provides a summary of the results of suchanalysis and, specifically, the analysis of the patient'santeroposterior and vertical movements.

Right Right Left Molar Canine Midline Canine Left Molar AnteroposteriorMovements (mm) Maxilla 1.5 mm left 1.0 distal Mandible 3 mesial 2.5mesial 0.5 mm left   2 mesial 3 mesial Vertical Movements (mm) Maxilla1.5 mm 2.0 mm occlusal occlusal Mandible Curve of Spee (mm) MaxillaMandible 3 mm

The diagnostic process of this Example further entails the followinganalyses of the patient: (1) a root tip analysis (results are summarizedin FIG. 3); (2) a tooth torque analysis (results are summarized in FIG.4); (3) an arch length analysis (results are summarized in FIG. 5); and(4) a Bolton analysis (the results of which are summarized in the tablebelow).

Bolton Analysis Millimeters

Anterior (Bolton 6) mm Posterior (Bolton 12) mm Maxilla 2 mm deficient 2mm deficient Mandible

The invention provides that a series of image analyses may then beperformed, namely, an image analysis of a patient's frontal and profileplanes. The results captured in this Example are summarized in thetables below.

Frontal Analysis

Parameter Results Upper Third Within normal limits Middle Third Withinnormal limits Lower Third Decreased Maxillary Lip Within normal limitsMandibular Lip Within normal limits Smile Within normal limits GingivalDisplay Within normal limits Symmetry Within normal limits

Profile Analysis

Parameter Results Profile Convex Maxillary lip to E plane 1 mm Lipstrain Yes Lip competence Incompetent

As explained above, the invention provides that a patient and/orclinician (dentist or orthodontist) may specify certain additionalcriteria, which the server will consider in calculating a diagnosis andtreatment plan. The table below provides the criteria selected by thepatient in this Example.

Patient Preferences

Priority Parameter (Scale of 1-10 for Importance) Facial Aesthetics 9Comfort 2 Treatment Time 7 Removable Appliances 1 Aesthetic Braces 2Orthognathic Surgery 1 Cost 5

The foregoing patient data, measurements, and preferences aresubsequently provided to the server, via the centralized websitedescribed herein. Using one or more artificial intelligence algorithms,such as the algorithms described herein (or combinations thereof), aswell as (i) information derived from textbooks and scientific literatureand (ii) dynamic results derived from ongoing and completed patienttreatments, the server calculates one or more diagnoses for the patient,along with an associated probability value (which is indicative of therelative accuracy of each diagnosis). Three diagnoses, and associatedprobability values, for this Example are listed below.

-   -   Diagnosis One: Class II Malocclusion (85%)    -   Diagnosis Two: Class I Malocclusion (14%)    -   Diagnosis Three: Class III Malocclusion (1%)

In addition, based on the foregoing patient data, measurements,preferences, information, and diagnoses, the server calculates one ormore proposed treatment regimens for the patient, along with aprobability value that is correlated with a relative likelihood of therelevant treatment approach, corrective appliance, or a combinationthereof, being effective to reorient the patient's teeth to the desiredlocation and position. The list of proposed treatment regimens, andcorresponding probability values, calculated in this Example is providedbelow.

-   -   Growth Modification (61%)    -   Mandibular Extractions (72%)    -   Maxillary Extractions (58%)    -   Removable Appliances (8%)    -   Fixed Appliances (92%)    -   Retainers (99%)

In this Example, the server further calculated the average probablytreatment time to be about 26.5 months.

Although illustrative embodiments of the present invention have beendescribed herein, it should be understood that the invention is notlimited to those described, and that various other changes ormodifications may be made by one skilled in the art without departingfrom the scope or spirit of the invention.

What is claimed is:
 1. A method for diagnosing and identifying atreatment for an orthodontic condition, which comprises: (a) providing aserver on which a centralized website is hosted, wherein the server isconfigured to receive patient data through the website; (b) providing adatabase that comprises or has access to (i) information derived fromtextbooks and scientific literature and (ii) dynamic results derivedfrom ongoing and completed patient treatments; (c) operating at leastone computer program within the server, which is capable of analyzingthe patient data and identifying at least one diagnosis of theorthodontic condition based on said (i) information derived fromtextbooks and scientific literature and (ii) dynamic results derivedfrom ongoing and completed patient treatments; (d) assigning aprobability value to the at least one diagnosis, wherein the probabilityvalue represents a likelihood that the diagnosis is accurate; and (e)instructing the computer program to identify at least one treatmentapproach, a corrective appliance, or a combination thereof for the atleast one diagnosis.
 2. The method of claim 1, wherein the server isconfigured to identify a location and position of a plurality of teethin the patient data in two-dimensional space.
 3. The method of claim 1,wherein the server is configured to identify a location and position ofa plurality of teeth in the patient data in three-dimensional space. 4.The method of claim 3, which further comprises assigning coordinates toeach tooth within the plurality of teeth, wherein said coordinates arecorrelated to a location and position of each tooth.
 5. The method ofclaim 4, further comprising: (a) comparing the coordinates for each ofthe plurality of teeth to a table contained within the database, whereinthe table comprises a series of diagnostic data sets with eachdiagnostic data set comprising coordinates, or a range of coordinates,which are correlated with (i) a known location and position of aplurality of teeth and (ii) a previously diagnosed orthodonticcondition; (b) instructing the server to identify a diagnostic data setcontained within said database which represents a statistical best fit,or most closely resembles, the coordinates for the plurality of teeth ofthe patient; and (c) instructing the server to diagnosis the orthodonticcondition based on the diagnostic data set identified in step (b). 6.The method of claim 5, wherein the probability value that is assigned tothe at least one diagnosis is based, at least in part, on a confidencelevel that has been assigned to the diagnostic data set which the serveridentifies as the statistical best fit for the coordinates for theplurality of teeth of the patient.
 7. The method of claim 6, whichfurther comprises instructing the computer program to identify at leastone treatment approach, a corrective appliance, or a combination thereoffor the at least one diagnosis, by instructing the server to: (a)calculate a set of target coordinates, which represent a desired andcorrected location and position of each tooth in the plurality of teethof the patient; and (b) identify at least one treatment approach, acorrective appliance, or a combination thereof, which will be effectiveto reorient the plurality of teeth towards a location and positionrepresented by the target coordinates.
 8. The method of claim 7, whichfurther comprises instructing the server to calculate a probabilityvalue that is correlated with a relative likelihood of the at least onetreatment approach, corrective appliance, or a combination thereof,being effective to reorient the plurality of teeth to a location andposition represented by the target coordinates.
 9. The method of claim8, wherein the step of identifying at least one diagnosis of theorthodontic condition employs an application of at least one artificialintelligence algorithm.
 10. The method of claim 9, wherein the step ofidentifying at least one treatment approach, a corrective appliance, ora combination thereof for the at least one diagnosis, employs anapplication of at least one artificial intelligence algorithm.
 11. Asystem for diagnosing and identifying a treatment for an orthodonticcondition, which comprises: (a) a server on which a centralized websiteis hosted, wherein the server is configured to receive patient datathrough the website; (b) a database that comprises or has access to (i)information derived from textbooks and scientific literature and (ii)dynamic results derived from ongoing and completed patient treatments;and (c) at least one computer program housed within or accessible by theserver, which is capable of analyzing the patient data and identifyingat least one diagnosis of the orthodontic condition based on said (i)information derived from textbooks and scientific literature and (ii)dynamic results derived from ongoing and completed patient treatments,wherein the server is configured to be capable of: (i) assigning aprobability value to the at least one diagnosis, wherein the probabilityvalue represents a likelihood that the diagnosis is accurate; and (ii)instructing the computer program to identify at least one treatmentapproach, a corrective appliance, or a combination thereof for the atleast one diagnosis.
 12. The system of claim 11, wherein the server isconfigured to identify a location and position of a plurality of teethin the patient data in two-dimensional space.
 13. The system of claim11, wherein the server is configured to identify a location and positionof a plurality of teeth in the patient data in three-dimensional space.14. The system of claim 13, wherein the server is further configured toassign coordinates to each tooth within the plurality of teeth, whereinsaid coordinates are correlated to the location and position of eachtooth.
 15. The system of claim 14, wherein the server is furtherconfigured to: (a) compare the coordinates for each of the plurality ofteeth to a table contained within the database, wherein the tablecomprises a series of diagnostic data sets with each diagnostic data setcomprising coordinates, or a range of coordinates, which are correlatedwith (i) a known location and position of a plurality of teeth and (ii)a previously diagnosed orthodontic condition; (b) identify a diagnosticdata set contained within said database which represents a statisticalbest fit, or most closely resembles, the coordinates for the pluralityof teeth of the patient; and (c) diagnose the orthodontic conditionbased on the diagnostic data set identified in paragraph (b).
 16. Thesystem of claim 15, wherein the probability value that is assigned tothe at least one diagnosis is based, at least in part, on a confidencelevel that has been assigned to the diagnostic data set which the serveridentifies as the statistical best fit for the coordinates for theplurality of teeth of the patient.
 17. The system of claim 16, whereinthe computer program is configured to identify at least one treatmentapproach, a corrective appliance, or a combination thereof for the atleast one diagnosis, by instructing the server to: (a) calculate a setof target coordinates, which represent a desired and corrected locationand position of each tooth in the plurality of teeth of the patient; and(b) identify at least one treatment approach, a corrective appliance, ora combination thereof, which will be effective to reorient the pluralityof teeth towards a location and position represented by the targetcoordinates.
 18. The system of claim 17, wherein the server is furtherconfigured to calculate a probability value that is correlated with arelative likelihood of the at least one treatment approach, correctiveappliance, or a combination thereof, being effective to reorient theplurality of teeth to a location and position represented by the targetcoordinates.
 19. The system of claim 18, wherein the server isconfigured to employ at least one artificial intelligence algorithm whenidentifying at least one diagnosis of the orthodontic condition.
 20. Thesystem of claim 19, wherein the server is configured to employ at leastone artificial intelligence algorithm when identifying at least onetreatment approach, a corrective appliance, or a combination thereof forthe at least one diagnosis.